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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.1.</b>
                        
                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
                        第9节：数组操作和文件操作作业.md
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                            <b>2.13.</b>
                        
                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
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            </ul>
            
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                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
                    </a>
            
            
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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            </ul>
            
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                            <b>8.</b>
                        
                        第八章：补充
                    </a>
            
            
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                        第3节：bokeh库
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                        <h1 id="&#x6761;&#x5F62;&#x56FE;">&#x6761;&#x5F62;&#x56FE;</h1>
<p>&#x6761;&#x5F62;&#x56FE;&#x7684;&#x7ED8;&#x5236;&#x65B9;&#x5F0F;&#x8DDF;&#x6298;&#x7EBF;&#x56FE;&#x975E;&#x5E38;&#x7684;&#x7C7B;&#x4F3C;&#xFF0C;&#x53EA;&#x4E0D;&#x8FC7;&#x662F;&#x6362;&#x6210;&#x4E86;<code>plt.bar</code>&#x65B9;&#x6CD5;&#x3002;<code>plt.bar</code>&#x65B9;&#x6CD5;&#x6709;&#x4EE5;&#x4E0B;&#x5E38;&#x7528;&#x53C2;&#x6570;&#xFF1A;  </p>
<ol>
<li><code>x</code>&#xFF1A;&#x4E00;&#x4E2A;&#x6570;&#x7EC4;&#x6216;&#x8005;&#x5217;&#x8868;&#xFF0C;&#x4EE3;&#x8868;&#x9700;&#x8981;&#x7ED8;&#x5236;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x7684;x&#x8F74;&#x7684;&#x5750;&#x6807;&#x70B9;&#x3002;  </li>
<li><code>height</code>&#xFF1A;&#x4E00;&#x4E2A;&#x6570;&#x7EC4;&#x6216;&#x8005;&#x5217;&#x8868;&#xFF0C;&#x4EE3;&#x8868;&#x9700;&#x8981;&#x7ED8;&#x5236;&#x7684;&#x6761;&#x5F62;&#x56FE;y&#x8F74;&#x7684;&#x5750;&#x6807;&#x70B9;&#x3002;  </li>
<li><code>width</code>&#xFF1A;&#x6BCF;&#x4E00;&#x4E2A;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x5BBD;&#x5EA6;&#xFF0C;&#x9ED8;&#x8BA4;&#x662F;0.8&#x7684;&#x5BBD;&#x5EA6;&#x3002;  </li>
<li><code>bottom</code>&#xFF1A;<code>y</code>&#x8F74;&#x7684;&#x57FA;&#x7EBF;&#xFF0C;&#x9ED8;&#x8BA4;&#x662F;0&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x8DDD;&#x79BB;&#x5E95;&#x90E8;&#x4E3A;0.  </li>
<li><code>align</code>&#xFF1A;&#x5BF9;&#x9F50;&#x65B9;&#x5F0F;&#xFF0C;&#x9ED8;&#x8BA4;&#x662F;<code>center</code>&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x8DDF;&#x6307;&#x5B9A;&#x7684;<code>x</code>&#x5750;&#x6807;&#x5C45;&#x4E2D;&#x5BF9;&#x9F50;&#xFF0C;&#x8FD8;&#x6709;&#x4E3A;<code>edge</code>&#xFF0C;&#x9760;&#x8FB9;&#x5BF9;&#x9F50;&#xFF0C;&#x5177;&#x4F53;&#x9760;&#x53F3;&#x8FB9;&#x8FD8;&#x662F;&#x9760;&#x5DE6;&#x8FB9;&#xFF0C;&#x770B;<code>width</code>&#x7684;&#x6B63;&#x8D1F;&#x3002;  </li>
<li><code>color</code>&#xFF1A;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x989C;&#x8272;&#x3002;</li>
</ol>
<p>&#x8FD4;&#x56DE;&#x503C;&#x4E3A;<code>BarContainer</code>&#xFF0C;&#x662F;&#x4E00;&#x4E2A;&#x5B58;&#x50A8;&#x4E86;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x5BB9;&#x5668;&#xFF0C;&#x800C;&#x6761;&#x5F62;&#x56FE;&#x5B9E;&#x9645;&#x4E0A;&#x7684;&#x7C7B;&#x578B;&#x662F;<code>matplotlib.patches.Rectangle</code>&#x5BF9;&#x8C61;&#x3002;</p>
<p>&#x66F4;&#x591A;&#x53C2;&#x8003;&#xFF1A;<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar" target="_blank">https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html#matplotlib.pyplot.bar</a></p>
<h2 id="&#x6761;&#x5F62;&#x56FE;&#x7684;&#x7ED8;&#x5236;&#xFF1A;">&#x6761;&#x5F62;&#x56FE;&#x7684;&#x7ED8;&#x5236;&#xFF1A;</h2>
<p>&#x6BD4;&#x5982;&#x73B0;&#x5728;&#x6709;<code>2019</code>&#x5E74;&#x8D3A;&#x5C81;&#x7247;&#x7968;&#x623F;&#x7684;&#x6570;&#x636E;&#xFF08;&#x6570;&#x636E;&#x6765;&#x6E90;&#xFF1A;<a href="https://piaofang.maoyan.com/dashboard&#xFF09;&#xFF1A;" target="_blank">https://piaofang.maoyan.com/dashboard&#xFF09;&#xFF1A;</a></p>
<pre><code class="lang-python"><span class="hljs-comment">#&#x7968;&#x623F;&#x5355;&#x4F4D;&#x4EBF;&#x5143;</span>
movies = {
    <span class="hljs-string">&quot;&#x6D41;&#x6D6A;&#x5730;&#x7403;&quot;</span>:<span class="hljs-number">40.78</span>,
    <span class="hljs-string">&quot;&#x98DE;&#x9A70;&#x4EBA;&#x751F;&quot;</span>:<span class="hljs-number">15.77</span>,
    <span class="hljs-string">&quot;&#x75AF;&#x72C2;&#x7684;&#x5916;&#x661F;&#x4EBA;&quot;</span>:<span class="hljs-number">20.83</span>,
    <span class="hljs-string">&quot;&#x65B0;&#x559C;&#x5267;&#x4E4B;&#x738B;&quot;</span>:<span class="hljs-number">6.10</span>,
    <span class="hljs-string">&quot;&#x5EC9;&#x653F;&#x98CE;&#x4E91;&quot;</span>:<span class="hljs-number">1.10</span>,
    <span class="hljs-string">&quot;&#x795E;&#x63A2;&#x84B2;&#x677E;&#x9F84;&quot;</span>:<span class="hljs-number">1.49</span>,
    <span class="hljs-string">&quot;&#x5C0F;&#x732A;&#x4F69;&#x5947;&#x8FC7;&#x5927;&#x5E74;&quot;</span>:<span class="hljs-number">1.22</span>,
    <span class="hljs-string">&quot;&#x718A;&#x51FA;&#x6CA1;&#xB7;&#x539F;&#x59CB;&#x65F6;&#x4EE3;&quot;</span>:<span class="hljs-number">6.71</span>
}
</code></pre>
<p>&#x7528;&#x6761;&#x5F62;&#x56FE;&#x7ED8;&#x5236;&#x6BCF;&#x90E8;&#x7535;&#x5F71;&#x53CA;&#x5176;&#x7968;&#x623F;&#x7684;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">movies = {
    <span class="hljs-string">&quot;&#x6D41;&#x6D6A;&#x5730;&#x7403;&quot;</span>:<span class="hljs-number">40.78</span>,
    <span class="hljs-string">&quot;&#x98DE;&#x9A70;&#x4EBA;&#x751F;&quot;</span>:<span class="hljs-number">15.77</span>,
    <span class="hljs-string">&quot;&#x75AF;&#x72C2;&#x7684;&#x5916;&#x661F;&#x4EBA;&quot;</span>:<span class="hljs-number">20.83</span>,
    <span class="hljs-string">&quot;&#x65B0;&#x559C;&#x5267;&#x4E4B;&#x738B;&quot;</span>:<span class="hljs-number">6.10</span>,
    <span class="hljs-string">&quot;&#x5EC9;&#x653F;&#x98CE;&#x4E91;&quot;</span>:<span class="hljs-number">1.10</span>,
    <span class="hljs-string">&quot;&#x795E;&#x63A2;&#x84B2;&#x677E;&#x9F84;&quot;</span>:<span class="hljs-number">1.49</span>,
    <span class="hljs-string">&quot;&#x5C0F;&#x732A;&#x4F69;&#x5947;&#x8FC7;&#x5927;&#x5E74;&quot;</span>:<span class="hljs-number">1.22</span>,
    <span class="hljs-string">&quot;&#x718A;&#x51FA;&#x6CA1;&#xB7;&#x539F;&#x59CB;&#x65F6;&#x4EE3;&quot;</span>:<span class="hljs-number">6.71</span>
}
plt.bar(np.arange(len(movies)),list(movies.keys()))
plt.xticks(np.arange(len(movies)),list(movies.keys()),fontproperties=font)
plt.grid()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x7535;&#x5F71;&#x6761;&#x5F62;&#x56FE;.png" alt=""><br>&#x5176;&#x4E2D;<code>xticks</code>&#x548C;<code>yticks</code>&#x7684;&#x7528;&#x6CD5;&#x8DDF;&#x4E4B;&#x524D;&#x7684;&#x6298;&#x7EBF;&#x56FE;&#x4E00;&#x6837;&#x3002;&#x8FD9;&#x91CC;&#x65B0;&#x51FA;&#x73B0;&#x7684;&#x65B9;&#x6CD5;&#x662F;<code>bar</code>&#xFF0C;<code>bar</code>&#x5E38;&#x7528;&#x7684;&#x6709;3&#x4E2A;&#x53C2;&#x6570;&#xFF0C;&#x5206;&#x522B;&#x662F;<code>x</code>&#xFF08;x&#x8F74;&#x7684;&#x5750;&#x6807;&#x70B9;&#xFF09;,<code>y</code>&#xFF08;y&#x8F74;&#x7684;&#x5750;&#x6807;&#x70B9;&#xFF09;&#x4EE5;&#x53CA;<code>width</code>&#xFF08;&#x6761;&#x5F62;&#x7684;&#x5BBD;&#x5EA6;&#xFF09;&#x3002;</p>
<h2 id="&#x6A2A;&#x5411;&#x6761;&#x5F62;&#x56FE;&#xFF1A;">&#x6A2A;&#x5411;&#x6761;&#x5F62;&#x56FE;&#xFF1A;</h2>
<p>&#x6A2A;&#x5411;&#x6761;&#x5F62;&#x56FE;&#x9700;&#x8981;&#x4F7F;&#x7528;<code>plt.barh</code>&#x8FD9;&#x4E2A;&#x65B9;&#x6CD5;&#x8DDF;<code>bar</code>&#x975E;&#x5E38;&#x7684;&#x7C7B;&#x4F3C;&#xFF0C;&#x53EA;&#x4E0D;&#x8FC7;&#x628A;&#x65B9;&#x5411;&#x8FDB;&#x884C;&#x65CB;&#x8F6C;&#x3002;&#x53C2;&#x6570;&#x8DDF;<code>bar</code>&#x7C7B;&#x4F3C;&#xFF0C;&#x4F46;&#x4E5F;&#x6709;&#x533A;&#x522B;&#x3002;&#x5982;&#x4E0B;&#xFF1A;  </p>
<ol>
<li><code>y</code>&#xFF1A;&#x6570;&#x7EC4;&#x6216;&#x5217;&#x8868;&#xFF0C;&#x4EE3;&#x8868;&#x9700;&#x8981;&#x7ED8;&#x5236;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x5728;<code>y</code>&#x8F74;&#x4E0A;&#x7684;&#x5750;&#x6807;&#x70B9;&#x3002;  </li>
<li><code>width</code>&#xFF1A;&#x6570;&#x7EC4;&#x6216;&#x5217;&#x8868;&#xFF0C;&#x4EE3;&#x8868;&#x9700;&#x8981;&#x7ED8;&#x5236;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x5728;<code>x</code>&#x8F74;&#x4E0A;&#x7684;&#x503C;&#xFF08;&#x4E5F;&#x5C31;&#x662F;&#x957F;&#x5EA6;&#xFF09;&#x3002;  </li>
<li><code>height</code>&#xFF1A;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x9AD8;&#x5EA6;&#xFF0C;&#x9ED8;&#x8BA4;&#x662F;0.8&#x3002;  </li>
<li><code>left</code>&#xFF1A;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x57FA;&#x7EBF;&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x8DDD;&#x79BB;y&#x8F74;&#x7684;&#x8DDD;&#x79BB;&#x3002;  </li>
<li>&#x5176;&#x4ED6;&#x53C2;&#x6570;&#x8DDF;<code>bar</code>&#x4E00;&#x6837;&#x3002;</li>
</ol>
<p>&#x8FD4;&#x56DE;&#x503C;&#x4E5F;&#x662F;<code>BarContainer</code>&#x5BB9;&#x5668;&#x5BF9;&#x8C61;&#x3002;</p>
<p>&#x8FD8;&#x662F;&#x4EE5;&#x4EE5;&#x4E0A;&#x6570;&#x636E;&#x4E3A;&#x4F8B;&#xFF0C;&#x5C06;&#x7535;&#x5F71;&#x540D;&#x548C;&#x7968;&#x623F;&#x53CD;&#x8F6C;&#x4E00;&#x4E0B;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">movies = {
    <span class="hljs-string">&quot;&#x6D41;&#x6D6A;&#x5730;&#x7403;&quot;</span>:<span class="hljs-number">40.78</span>,
    <span class="hljs-string">&quot;&#x98DE;&#x9A70;&#x4EBA;&#x751F;&quot;</span>:<span class="hljs-number">15.77</span>,
    <span class="hljs-string">&quot;&#x75AF;&#x72C2;&#x7684;&#x5916;&#x661F;&#x4EBA;&quot;</span>:<span class="hljs-number">20.83</span>,
    <span class="hljs-string">&quot;&#x65B0;&#x559C;&#x5267;&#x4E4B;&#x738B;&quot;</span>:<span class="hljs-number">6.10</span>,
    <span class="hljs-string">&quot;&#x5EC9;&#x653F;&#x98CE;&#x4E91;&quot;</span>:<span class="hljs-number">1.10</span>,
    <span class="hljs-string">&quot;&#x795E;&#x63A2;&#x84B2;&#x677E;&#x9F84;&quot;</span>:<span class="hljs-number">1.49</span>,
    <span class="hljs-string">&quot;&#x5C0F;&#x732A;&#x4F69;&#x5947;&#x8FC7;&#x5927;&#x5E74;&quot;</span>:<span class="hljs-number">1.22</span>,
    <span class="hljs-string">&quot;&#x718A;&#x51FA;&#x6CA1;&#xB7;&#x539F;&#x59CB;&#x65F6;&#x4EE3;&quot;</span>:<span class="hljs-number">6.71</span>
}
plt.barh(np.arange(len(movies)),list(movies.values()))
plt.yticks(np.arange(len(movies)),list(movies.keys()),fontproperties=font)
plt.grid()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x7535;&#x5F71;&#x6A2A;&#x5411;&#x6761;&#x5F62;&#x56FE;.png" alt=""></p>
<h2 id="&#x5206;&#x7EC4;&#x6761;&#x5F62;&#x56FE;&#xFF1A;">&#x5206;&#x7EC4;&#x6761;&#x5F62;&#x56FE;&#xFF1A;</h2>
<p>&#x73B0;&#x5728;&#x6709;&#x4E00;&#x7EC4;&#x6570;&#x636E;&#xFF0C;&#x662F;2019&#x5E74;&#x6625;&#x8282;&#x8D3A;&#x5C81;&#x7247;&#x524D;&#x4E94;&#x5929;&#x7684;&#x7535;&#x5F71;&#x7968;&#x623F;&#x8BB0;&#x5F55;&#x3002;<br>&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">movies = {
    <span class="hljs-string">&quot;&#x6D41;&#x6D6A;&#x5730;&#x7403;&quot;</span>:[<span class="hljs-number">2.01</span>,<span class="hljs-number">4.59</span>,<span class="hljs-number">7.99</span>,<span class="hljs-number">11.83</span>,<span class="hljs-number">16</span>],
    <span class="hljs-string">&quot;&#x98DE;&#x9A70;&#x4EBA;&#x751F;&quot;</span>:[<span class="hljs-number">3.19</span>,<span class="hljs-number">5.08</span>,<span class="hljs-number">6.73</span>,<span class="hljs-number">8.10</span>,<span class="hljs-number">9.35</span>],
    <span class="hljs-string">&quot;&#x75AF;&#x72C2;&#x7684;&#x5916;&#x661F;&#x4EBA;&quot;</span>:[<span class="hljs-number">4.07</span>,<span class="hljs-number">6.92</span>,<span class="hljs-number">9.30</span>,<span class="hljs-number">11.29</span>,<span class="hljs-number">13.03</span>],
    <span class="hljs-string">&quot;&#x65B0;&#x559C;&#x5267;&#x4E4B;&#x738B;&quot;</span>:[<span class="hljs-number">2.72</span>,<span class="hljs-number">3.79</span>,<span class="hljs-number">4.45</span>,<span class="hljs-number">4.83</span>,<span class="hljs-number">5.11</span>],
    <span class="hljs-string">&quot;&#x5EC9;&#x653F;&#x98CE;&#x4E91;&quot;</span>:[<span class="hljs-number">0.56</span>,<span class="hljs-number">0.74</span>,<span class="hljs-number">0.83</span>,<span class="hljs-number">0.88</span>,<span class="hljs-number">0.92</span>],
    <span class="hljs-string">&quot;&#x795E;&#x63A2;&#x84B2;&#x677E;&#x9F84;&quot;</span>:[<span class="hljs-number">0.66</span>,<span class="hljs-number">0.95</span>,<span class="hljs-number">1.10</span>,<span class="hljs-number">1.17</span>,<span class="hljs-number">1.23</span>],
    <span class="hljs-string">&quot;&#x5C0F;&#x732A;&#x4F69;&#x5947;&#x8FC7;&#x5927;&#x5E74;&quot;</span>:[<span class="hljs-number">0.58</span>,<span class="hljs-number">0.81</span>,<span class="hljs-number">0.94</span>,<span class="hljs-number">1.01</span>,<span class="hljs-number">1.07</span>],
    <span class="hljs-string">&quot;&#x718A;&#x51FA;&#x6CA1;&#xB7;&#x539F;&#x59CB;&#x65F6;&#x4EE3;&quot;</span>:[<span class="hljs-number">1.13</span>,<span class="hljs-number">1.96</span>,<span class="hljs-number">2.73</span>,<span class="hljs-number">3.42</span>,<span class="hljs-number">4.05</span>]
}
plt.figure(figsize=(<span class="hljs-number">20</span>,<span class="hljs-number">8</span>))
width = <span class="hljs-number">0.75</span>
bin_width = width/<span class="hljs-number">5</span>
movie_pd = pd.DataFrame(movies)
ind = np.arange(<span class="hljs-number">0</span>,len(movies))

<span class="hljs-comment"># &#x7B2C;&#x4E00;&#x79CD;&#x65B9;&#x6848;</span>
<span class="hljs-comment"># first_day = movie_pd.iloc[0]</span>
<span class="hljs-comment"># plt.bar(ind-bin_width*2,first_day,width=bin_width,label=&apos;&#x7B2C;&#x4E00;&#x5929;&apos;)</span>

<span class="hljs-comment"># second_day = movie_pd.iloc[1]</span>
<span class="hljs-comment"># plt.bar(ind-bin_width,second_day,width=bin_width,label=&apos;&#x7B2C;&#x4E8C;&#x5929;&apos;)</span>

<span class="hljs-comment"># third_day = movie_pd.iloc[2]</span>
<span class="hljs-comment"># plt.bar(ind,third_day,width=bin_width,label=&apos;&#x7B2C;&#x4E09;&#x5929;&apos;)</span>

<span class="hljs-comment"># four_day = movie_pd.iloc[3]</span>
<span class="hljs-comment"># plt.bar(ind+bin_width,four_day,width=bin_width,label=&apos;&#x7B2C;&#x56DB;&#x5929;&apos;)</span>

<span class="hljs-comment"># five_day = movie_pd.iloc[4]</span>
<span class="hljs-comment"># plt.bar(ind+bin_width*2,five_day,width=bin_width,label=&apos;&#x7B2C;&#x4E94;&#x5929;&apos;)</span>

<span class="hljs-comment"># &#x7B2C;&#x4E8C;&#x79CD;&#x65B9;&#x6848;</span>
<span class="hljs-keyword">for</span> index <span class="hljs-keyword">in</span> movie_pd.index:
    day_tickets = movie_pd.iloc[index]
    xs = ind-(bin_width*(<span class="hljs-number">2</span>-index))
    plt.bar(xs,day_tickets,width=bin_width,label=<span class="hljs-string">&quot;&#x7B2C;%d&#x5929;&quot;</span>%(index+<span class="hljs-number">1</span>))
    <span class="hljs-keyword">for</span> ticket,x <span class="hljs-keyword">in</span> zip(day_tickets,xs):
        plt.annotate(ticket,xy=(x,ticket),xytext=(x-<span class="hljs-number">0.1</span>,ticket+<span class="hljs-number">0.1</span>))

<span class="hljs-comment"># &#x8BBE;&#x7F6E;&#x56FE;&#x4F8B;</span>
plt.legend(prop=font)
plt.ylabel(<span class="hljs-string">&quot;&#x5355;&#x4F4D;&#xFF1A;&#x4EBF;&quot;</span>,fontproperties=font)
plt.title(<span class="hljs-string">&quot;&#x6625;&#x8282;&#x524D;5&#x5929;&#x7535;&#x5F71;&#x7968;&#x623F;&#x8BB0;&#x5F55;&quot;</span>,fontproperties=font)
<span class="hljs-comment"># &#x8BBE;&#x7F6E;x&#x8F74;&#x7684;&#x5750;&#x6807;</span>
plt.xticks(ind,movie_pd.columns,fontproperties=font)
plt.xlim
plt.grid(<span class="hljs-keyword">True</span>)
plt.show()
</code></pre>
<p>&#x793A;&#x4F8B;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x5206;&#x7EC4;&#x6761;&#x5F62;&#x56FE;.png" alt=""></p>
<h2 id="&#x5806;&#x53E0;&#x6761;&#x5F62;&#x56FE;&#xFF1A;">&#x5806;&#x53E0;&#x6761;&#x5F62;&#x56FE;&#xFF1A;</h2>
<p>&#x5806;&#x53E0;&#x6761;&#x5F62;&#x56FE;&#xFF0C;&#x662F;&#x5C06;&#x4E00;&#x7EC4;&#x76F8;&#x5173;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x5806;&#x53E0;&#x5728;&#x4E00;&#x8D77;&#x8FDB;&#x884C;&#x6BD4;&#x8F83;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x3002;&#x6BD4;&#x5982;&#x4EE5;&#x4E0B;&#x6848;&#x4F8B;&#xFF1A;</p>
<pre><code class="lang-python">menMeans = (<span class="hljs-number">20</span>, <span class="hljs-number">35</span>, <span class="hljs-number">30</span>, <span class="hljs-number">35</span>, <span class="hljs-number">27</span>)
womenMeans = (<span class="hljs-number">25</span>, <span class="hljs-number">32</span>, <span class="hljs-number">34</span>, <span class="hljs-number">20</span>, <span class="hljs-number">25</span>)
groupNames = (<span class="hljs-string">&apos;G1&apos;</span>,<span class="hljs-string">&apos;G2&apos;</span>,<span class="hljs-string">&apos;G3&apos;</span>,<span class="hljs-string">&apos;G4&apos;</span>,<span class="hljs-string">&apos;G5&apos;</span>)
xs = np.arange(len(menMeans))
plt.bar(xs,menMeans)
plt.bar(xs,womenMeans,bottom=menMeans)
plt.xticks(xs,groupNames)
plt.show()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x5806;&#x53E0;&#x6761;&#x5F62;&#x56FE;.png" alt=""><br>&#x5728;&#x7ED8;&#x5236;&#x5973;&#x6027;&#x5F97;&#x5206;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x7684;&#x65F6;&#x5019;&#xFF0C;&#x56E0;&#x4E3A;&#x8981;&#x5806;&#x53E0;&#x5728;&#x7537;&#x6027;&#x5F97;&#x5206;&#x7684;&#x6761;&#x5F62;&#x56FE;&#x4E0A;&#xFF0C;&#x6240;&#x4EE5;&#x4F7F;&#x7528;&#x5230;&#x4E86;&#x4E00;&#x4E2A;<code>bottom</code>&#x53C2;&#x6570;&#xFF0C;&#x5C31;&#x662F;&#x8DDD;&#x79BB;<code>x</code>&#x8F74;&#x7684;&#x8DDD;&#x79BB;&#x3002;&#x901A;&#x8FC7;&#x5BF9;&#x8D34;&#x6761;&#x5F62;&#x56FE;&#xFF0C;&#x6211;&#x4EEC;&#x5C31;&#x53EF;&#x4EE5;&#x6E05;&#x695A;&#x7684;&#x77E5;&#x9053;&#xFF0C;&#x54EA;&#x4E00;&#x4E2A;&#x961F;&#x4F0D;&#x7684;&#x7EFC;&#x5408;&#x6392;&#x540D;&#x662F;&#x6700;&#x9AD8;&#x7684;&#xFF0C;&#x5E76;&#x4E14;&#x5728;&#x6BCF;&#x4E2A;&#x961F;&#x4F0D;&#x4E2D;&#x7537;&#x5973;&#x7684;&#x5F97;&#x5206;&#x60C5;&#x51B5;&#x3002;</p>
<h2 id="&#x6761;&#x5F62;&#x56FE;&#x5E94;&#x7528;&#x573A;&#x666F;&#xFF1A;">&#x6761;&#x5F62;&#x56FE;&#x5E94;&#x7528;&#x573A;&#x666F;&#xFF1A;</h2>
<ol>
<li>&#x6570;&#x91CF;&#x7EDF;&#x8BA1;&#x3002;</li>
<li>&#x9891;&#x7387;&#x7EDF;&#x8BA1;&#x3002;</li>
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